基于神经网络的模板匹配方法求正常星系红移  

Using Neural Networks Based Template Matching Method to Obtain Redshifts of Normal Galaxies

作　　者：许馨[1] 罗阿理[2] 吴福朝[1] 赵永恒[2] 

机构地区：[1]中国科学院自动化所国家模式识别实验室机器人视觉组,北京100080 [2]中国科学院国家天文台,北京100012

出　　处：《光谱学与光谱分析》2005年第6期996-1001,共6页Spectroscopy and Spectral Analysis

基　　金：国家"863"项目计划(2003AA133060);国家自然科学基金(60202013)资助项目

摘　　要：星系通常分为正常星系(NG)与活动星系(AG)两类。文章提出了一种自动获取NG红移的快速有效方法:(1)由NG模板根据红移范围Ⅰ:0 0～0 3与Ⅱ:0 3～0 5模拟得到两类星系样本,进行PCA变换获得样本特征向量;(2 )利用概率神经网络设计两类样本特征向量的Bayes分类器;(3)对于实际NG光谱数据,利用Bayes分类器进行分类确定其红移的范围,然后在此范围内进行模板匹配得到红移的准确值。与在整个红移范围内的模板匹配方法相比,此方法不但节省了5 0 %的模板匹配运算量,而且还大大提高了红移值测量的精度。文章研究结果对于大型光谱巡天所产生的海量数据的自动处理具有重要意义。Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is proposed in this paper, which consists of the following three main steps: (1) From the template of normal galaxy, the two sets of samples are simulated, one with the redshift of 0. 0-0. 3, the other of 0. 3-0. 5, then the PCA is used to extract the main components, and train samples are projected to the main component subspace to obtain characteristic spectra. (2) The characteristic spectra are used to train a Probabilistic Neural Network to obtain a Bayes classifier. (3) An unknown real NG spectrum is first inputted to this Bayes classifier to determine the possible range of redshift, then the template matching is invoked to locate the redshift value within the estimated range. Compared with the traditional template matching technique with an unconstrained range, our proposed method not only halves the computational load, but also increases the estimation accuracy. As a result, the proposed method is particularly useful for automatic spectrum processing produced from a large-scale sky survey project.

关 键 词：正常星系 主分量分析 概率神经网络 红移分类 模板匹配 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]